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About

Michele Grimaldi is an emerging researcher specializing in marine robotics, underwater simulation, and the application of machine learning — particularly reinforcement learning — to autonomous underwater systems. His work addresses one of the field's most pressing challenges: the high cost and logistical complexity of real-world underwater trials, which make robust simulation environments essential for advancing the discipline. Grimaldi's most significant contribution to date is URoBench, a standardized benchmark framework for evaluating reinforcement learning algorithms across underwater robot simulators, which has garnered 7 citations since its 2024 publication. Complementing this, his contributions to the Stonefish simulator project demonstrate a commitment to building machine learning-ready tools tailored to the demanding conditions of marine environments, accumulating a combined 8 citations across related publications. His more recent work on 3DSSDF explores underwater 3D reconstruction using signed distance functions, pushing the boundaries of sonar-based mapping for autonomous navigation in GPS-denied environments. Across his portfolio, Grimaldi consistently bridges the gap between simulation fidelity and practical machine learning deployment. Though early in his career, his focused contributions to underwater robotics infrastructure position him as a valuable voice in a rapidly growing and critically important research domain.